Remote Sensing of Snow Parameters: A Sensitivity Study of Retrieval Performance Based on Hyperspectral versus Multispectral Data

Elliot Pachniak, Wei Li, Tomonori Tanikawa, Charles Gatebe, Knut Stamnes

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Snow parameters have traditionally been retrieved using discontinuous, multi-band sensors; however, continuous hyperspectral sensors are now being developed as an alternative. In this paper, we investigate the performance of various sensor configurations using machine learning neural networks trained on a simulated dataset. Our results show improvements in the accuracy of retrievals of snow grain size and impurity concentration for continuous hyperspectral channel configurations. Retrieval accuracy of snow albedo was found to be similar for all channel configurations.

Original languageEnglish
Article number493
JournalAlgorithms
Volume16
Issue number10
DOIs
StatePublished - Oct 2023

Keywords

  • MODIS
  • SBG DO
  • SGLI
  • hyperspectral
  • machine learning
  • neural networks
  • remote sensing
  • snow

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